EG2025

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2D Neural Fields with Learned Discontinuities
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Liu, Chenxi; Wang, Siqi; Fisher, Matthew; Aneja, Deepali; Jacobson, Alec; Bousseau, Adrien; Day, Angela
Effective representation of 2D images is fundamental in digital image processing, where traditional methods like raster and vector graphics struggle with sharpness and textural complexity, respectively. Current neural fields offer high fidelity and resolution independence but require predefined meshes with known discontinuities, restricting their utility. We observe that by treating all mesh edges as potential discontinuities, we can represent the discontinuity magnitudes as continuous variables and optimize. We further introduce a novel discontinuous neural field model that jointly approximates the target image and recovers discontinuities. Through systematic evaluations, our neural field outperforms other methods that fit unknown discontinuities with discontinuous representations, exceeding Field of Junction and Boundary Attention by over 11dB in both denoising and super-resolution tasks and achieving 3.5× smaller Chamfer distances than Mumford-Shah-based methods. It also surpasses InstantNGP with improvements of more than 5dB (denoising) and 10dB (super-resolution). Additionally, our approach shows remarkable capability in approximating complex artistic and natural images and cleaning up diffusion-generated depth maps.
3D Gabor Splatting: Reconstruction of High-frequnecy Surface Texture using Gabor Noise
(The Eurographics Association, 2025) Watanabe, Haato; Tojo, Kenji; Umetani, Nobuyuki; Ceylan, Duygu; Li, Tzu-Mao
3D Gaussian splatting has experienced explosive popularity in the past few years in the field of novel view synthesis. The lightweight and differentiable representation of the radiance field using the Gaussian enables rapid and high-quality reconstruction and fast rendering. However, reconstructing objects with high-frequency surface textures (e.g., fine stripes) requires many skinny Gaussian kernels because each Gaussian represents only one color if viewed from one direction. Thus, reconstructing the stripes pattern, for example, requires Gaussians for at least the number of stripes. We present 3D Gabor splatting, which augments the Gaussian kernel to represent spatially high-frequency signals using Gabor noise. The Gabor kernel is a combination of a Gaussian term and spatially fluctuating wave functions, making it suitable for representing spatial high-frequency texture. We demonstrate that our 3D Gabor splatting can reconstruct various high-frequency textures on the objects.
3D Garments: Reconstructing Topologically Correct Geometry and High-Quality Texture from Two Garment Images
(The Eurographics Association, 2025) Heße, Lisa; Yadav, Sunil; Ceylan, Duygu; Li, Tzu-Mao
We present a fully integrated pipeline for generating topologically correct 3D meshes and high-fidelity textures of fashion garments. Our geometry reconstruction module takes two input images and employs a semi-signed distance field representation with shifted generalized winding numbers in a deep-learning framework to produce accurate, non-watertight meshes. To create realistic, high-resolution textures (up to 4K) that closely match the input, we combine diffusion-based inpainting with a differentiable renderer, further enhancing the quality through normal-guided projection to minimize projection distortions in the texture image. Our results demonstrate both precise geometry and richly detailed textures. In addition, we are making a portion of our high-quality training dataset publicly available, consisting of 250 lower-garment triangulated meshes with 4K textures.
3D Shape Analysis: From Classical Optimisation Methods to Feature Learning for Shape Matching
(The Eurographics Association, 2025) Amrani, Nafie El; Lennart, Bastian; Ehm, Viktoria; Laehner, Zorah; Bernard, Florian; Mantiuk, Rafal; Hildebrandt, Klaus
The field of 3D shape analysis is concerned with the extraction of ''useful'' information from geometric data. Shape analysis has a high relevance for a wide range of applications, such as autonomous driving, biomedicine, or augmented/virtual reality. A core task of 3D shape analysis is shape matching, i.e. identifying correspondences between given shapes. While traditional shape matching methods rely on optimising a task-specific objective function, modern shape matching oftentimes involves datadriven components. We will first introduce traditional methods for shape matching, starting with the linear assignment problem and the quadratic assignment problem. We then present product graph formalisms in different settings, including 2D to 2D, 2D to 3D or shape to image, and 3D to 3D shape matching. We then discuss recent developments in learning-based shape correspondence methods, from learning shape correspondence with topological data structures to spectral approaches that provide efficient structure and circumvent annotations altogether. Furthermore, we discuss the practical relevance of these methods to application domains in image-to-image and shape-to-image correspondence, medical imaging and surgical navigation, and discuss how recent developments in foundation models play a role in shape analysis. Finally, the tutorial will conclude by addressing the challenges of shape matching, including handling partial shapes, and will explore potential future directions in the field.
3DGS.zip: A survey on 3D Gaussian Splatting Compression Methods
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Bagdasarian, Milena T.; Knoll, Paul; Li, Yi-Hsin; Barthel, Florian; Hilsmann, Anna; Eisert, Peter; Morgenstern, Wieland; Memari, Pooran; Gryaditskaya, Yulia
3D Gaussian Splatting (3DGS) has emerged as a cutting-edge technique for real-time radiance field rendering, offering state-ofthe- art performance in terms of both quality and speed. 3DGS models a scene as a collection of three-dimensional Gaussians, with additional attributes optimized to conform to the scene's geometric and visual properties. Despite its advantages in rendering speed and image fidelity, 3DGS is limited by its significant storage and memory demands. These high demands make 3DGS impractical for mobile devices or headsets, reducing its applicability in important areas of computer graphics. To address these challenges and advance the practicality of 3DGS, this state-of-the-art report (STAR) provides a comprehensive and detailed examination of two complementary yet fundamentally distinct strategies: compression and compaction. Compression techniques focus on reducing the file size by encoding Gaussian attributes more efficiently. In contrast, compaction methods directly optimize the scene's structure by optimizing the number of Gaussian primitives. Notably, while methods in both categories aim to maintain or improve quality, each while minimizing its respective attributes-file size for compression and the number of Gaussians for compaction-compaction does not necessarily lead to smaller file sizes; it specifically targets improved efficiency during rendering, making it distinct from compression. We introduce the basic mathematical concepts underlying the analyzed methods, as well as key implementation details and design choices. Our report thoroughly discusses similarities and differences among the methods, as well as their respective advantages and disadvantages. We establish a consistent framework for comparing the surveyed methods based on key performance metrics and datasets. Specifically, since these methods have been developed in parallel and over a short period of time, currently, no comprehensive comparison exists. This survey, for the first time, presents a unified framework to evaluate 3DGS compression techniques. To facilitate the continuous monitoring of emerging methodologies, we maintain a dedicated website that will be regularly updated with new techniques and revisions of existing findings. Overall, this STAR provides an intuitive starting point for researchers interested in exploring the rapidly growing field of 3DGS compression. By comprehensively categorizing and evaluating existing compression and compaction strategies, our work advances the understanding and practical application of 3DGS in computationally constrained environments.
4-LEGS: 4D Language Embedded Gaussian Splatting
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Fiebelman, Gal; Cohen, Tamir; Morgenstern, Ayellet; Hedman, Peter; Averbuch-Elor, Hadar; Bousseau, Adrien; Day, Angela
The emergence of neural representations has revolutionized our means for digitally viewing a wide range of 3D scenes, enabling the synthesis of photorealistic images rendered from novel views. Recently, several techniques have been proposed for connecting these low-level representations with the high-level semantics understanding embodied within the scene. These methods elevate the rich semantic understanding from 2D imagery to 3D representations, distilling high-dimensional spatial features onto 3D space. In our work, we are interested in connecting language with a dynamic modeling of the world. We show how to lift spatio-temporal features to a 4D representation based on 3D Gaussian Splatting. This enables an interactive interface where the user can spatiotemporally localize events in the video from text prompts. We demonstrate our system on public 3D video datasets of people and animals performing various actions.
Adaptive Multi-view Radiance Caching for Heterogeneous Participating Media
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Stadlbauer, Pascal; Tatzgern, Wolfgang; Mueller, Joerg H.; Winter, Martin; Stojanovic, Robert; Weinrauch, Alexander; Steinberger, Markus; Bousseau, Adrien; Day, Angela
Achieving lifelike atmospheric effects, such as fog, is essential in creating immersive environments and poses a formidable challenge in real-time rendering. Highly realistic rendering of complex lighting interacting with dynamic fog can be very resourceintensive, due to light bouncing through a complex participating media multiple times. We propose an approach that uses a multi-layered spherical harmonics probe grid to share computations temporarily. In addition, this world-space storage enables the sharing of radiance data between multiple viewers. In the context of cloud rendering this means faster rendering and a significant enhancement in overall rendering quality with efficient resource utilization.
Advancing XR Education: Towards a Multimodal Human-Machine Interaction Course for Doctoral Students in Computer Science
(The Eurographics Association, 2025) Silva, Samuel; Marques, Bernardo; Mendes, Daniel; Rodrigues, Rui; Kuffner dos Anjos, Rafael; Rodriguez Echavarria, Karina
Nowadays, eXtended Reality (XR) has matured to the point where it seamlessly integrates various input and output modalities, enhancing the way users interact with digital environments. From traditional controllers and hand tracking to voice commands, eye tracking, and even biometric sensors, XR systems now offer more natural interactions. Similarly, output modalities have expanded beyond visual displays to include haptic feedback, spatial audio, and others, enriching the overall user experience. In this vein, as the field of XR becomes increasingly multimodal, the education process must also evolve to reflect these advancements. There is a growing need to incorporate additional modalities into the curriculum, helping students understand their relevance and practical applications. By exposing students to a diverse range of interaction techniques, they can better assess which modalities are most suitable for different contexts, enabling them to design more effective and human-centered solutions. This work describes an Advanced Human-Machine Interaction (HMI) course aimed at Doctoral Students in Computer Science. The primary objective is to provide students with the necessary knowledge in HMI by enabling them to articulate the fundamental concepts of the field, recognize and analyze the role of human factors, identify modern interaction methods and technologies, apply HCD principles to interactive system design and development, and implement appropriate methods for assessing interaction experiences across advanced HMI topics. In this vein, the course structure, the range of topics covered, assessment strategies, as well as the hardware and infrastructure employed are presented. Additionally, it highlights mini-projects, including flexibility for students to integrate their projects, fostering personalized and project-driven learning. The discussion reflects on the challenges inherent in keeping pace with this rapidly evolving field and emphasizes the importance of adapting to emerging trends. Finally, the paper outlines future directions and potential enhancements for the course.
All-frequency Full-body Human Image Relighting
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Tajima, Daichi; Kanamori, Yoshihiro; Endo, Yuki; Bousseau, Adrien; Day, Angela
Relighting of human images enables post-photography editing of lighting effects in portraits. The current mainstream approach uses neural networks to approximate lighting effects without explicitly accounting for the principle of physical shading. As a result, it often has difficulty representing high-frequency shadows and shading. In this paper, we propose a two-stage relighting method that can reproduce physically-based shadows and shading from low to high frequencies. The key idea is to approximate an environment light source with a set of a fixed number of area light sources. The first stage employs supervised inverse rendering from a single image using neural networks and calculates physically-based shading. The second stage then calculates shadow for each area light and sums up to render the final image. We propose to make soft shadow mapping differentiable for the area-light approximation of environment lighting. We demonstrate that our method can plausibly reproduce all-frequency shadows and shading caused by environment illumination, which have been difficult to reproduce using existing methods.
Approximate and Exact Buoyancy Calculation for Real-time Floating Simulation of Meshes
(The Eurographics Association, 2025) Fábián, Gábor; Ceylan, Duygu; Li, Tzu-Mao
In this paper, we present methods for simulating floatation of bodies represented by triangular meshes. The primary challenge in creating such a simulation is determining the buoyant force and its reference point. We propose 5 algorithms, 3 approximations and 2 exact methods, that enable the real-time calculation of buoyant forces. Each algorithm is based on rigorous physical and mathematical principles, performing calculations directly on the triangular mesh rather than its approximation. Finally, we test the accuracy and efficiency of these algorithms through simple examples.
Approximating Procedural Models of 3D Shapes with Neural Networks
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Hossain, Ishtiaque; Shen, I-Chao; Kaick, Oliver van; Bousseau, Adrien; Day, Angela
Procedural modeling is a popular technique for 3D content creation and offers a number of advantages over alternative techniques for modeling 3D shapes. However, given a procedural model, predicting the procedural parameters of existing data provided in different modalities can be challenging. This is because the data may be in a different representation than the one generated by the procedural model, and procedural models are usually not invertible, nor are they differentiable. In this paper, we address these limitations and introduce an invertible and differentiable representation for procedural models. We approximate parameterized procedures with a neural network architecture NNProc that learns both the forward and inverse mapping of the procedural model by aligning the latent spaces of shape parameters and shapes. The network is trained in a manner that is agnostic to the inner workings of the procedural model, implying that models implemented in different languages or systems can be used. We demonstrate how the proposed representation can be used for both forward and inverse procedural modeling. Moreover, we show how NNProc can be used in conjunction with optimization for applications such as shape reconstruction from an image or a 3D Gaussian Splatting.
ASMR: Adaptive Skeleton-Mesh Rigging and Skinning via 2D Generative Prior
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Hong, Seokhyeon; Choi, Soojin; Kim, Chaelin; Cha, Sihun; Noh, Junyong; Bousseau, Adrien; Day, Angela
Despite the growing accessibility of skeletal motion data, integrating it for animating character meshes remains challenging due to diverse configurations of both skeletons and meshes. Specifically, the body scale and bone lengths of the skeleton should be adjusted in accordance with the size and proportions of the mesh, ensuring that all joints are accurately positioned within the character mesh. Furthermore, defining skinning weights is complicated by variations in skeletal configurations, such as the number of joints and their hierarchy, as well as differences in mesh configurations, including their connectivity and shapes. While existing approaches have made efforts to automate this process, they hardly address the variations in both skeletal and mesh configurations. In this paper, we present a novel method for the automatic rigging and skinning of character meshes using skeletal motion data, accommodating arbitrary configurations of both meshes and skeletons. The proposed method predicts the optimal skeleton aligned with the size and proportion of the mesh as well as defines skinning weights for various meshskeleton configurations, without requiring explicit supervision tailored to each of them. By incorporating Diffusion 3D Features (Diff3F) as semantic descriptors of character meshes, our method achieves robust generalization across different configurations. To assess the performance of our method in comparison to existing approaches, we conducted comprehensive evaluations encompassing both quantitative and qualitative analyses, specifically examining the predicted skeletons, skinning weights, and deformation quality.
Audio-aided Character Control for Inertial Measurement Tracking
(The Eurographics Association, 2025) Jang, Hojun; Bae, Jinseok; Kim, Young Min; Ceylan, Duygu; Li, Tzu-Mao
Physics-based character control generates realistic motion dynamics by leveraging kinematic priors from large-scale data within a simulation engine. The simulated motion respects physical plausibility, while dynamic cues like contacts and forces guide compelling human-scene interaction. However, leveraging audio cues, which can capture physical contacts in a costeffective way, has been less explored in animating human motions. In this work, we demonstrate that audio inputs can enhance accuracy in predicting footsteps and capturing human locomotion dynamics. Experiments validate that audio-aided control from sparse observations (e.g., an IMU sensor on a VR headset) enhances the prediction accuracy of contact dynamics and motion tracking, offering a practical auxiliary signal for robotics, gaming, and virtual environments.
Automated Skeleton Transformations on 3D Tree Models Captured from an RGB Video
(The Eurographics Association, 2025) Michels, Joren; Moonen, Steven; GÜNEY, ENES; Temsamani, Abdellatif Bey; Michiels, Nick; Ceylan, Duygu; Li, Tzu-Mao
A lot of work has been done surrounding the generation of realistically looking 3D models of trees. In most cases, L-systems are used to create variations of specific trees from a set of rules. While achieving good results, these techniques require knowledge of the structure of the tree to construct generative rules. We propose a system that can create variations of trees captured by a single RGB video. Using our method, plausible variations can be created without needing prior knowledge of the specific type of tree. This results in a fast and cost-efficient way to generate trees that resemble their real-life counterparts.
Automatic Image-Based Coral Polyp Analysis through Multi-View Instance Segmentation
(The Eurographics Association, 2025) Dutta, Somnath; Pavoni, Gaia; Corsini, Massimiliano; Ganovelli, Fabio; Cignoni, Paolo; Rossi, Paolo; Cenni, Elena; Simonini, Roberto; Grassi, Francesca; Cassanelli, Davide; Cattini, Stefano; Rovati, Luigi; Capra, Alessandro; Castagnetti, Cristina; Günther, Tobias; Montazeri, Zahra
We present an automated framework for counting and measuring the polyps of Cladocora caespitosa, a Mediterranean reefbuilding coral. To our knowledge, the most practical method for counting polyps currently involves ecologists' visual inspection of a 3D model. However, measuring polyps from the model can lead to inaccuracies due to distortions in the reconstruction. Our method integrates deep learning-based instance segmentation on 2D images with 3D models for unique polyp identification, ensuring precise biometric extraction. The proposed pipeline automates polyp detection, counting, and measurement while overcoming the limitations of manual in situ methods. Laboratory validation demonstrates its accuracy and efficiency, paving the way for scalable, high-resolution phenotyping, and field monitoring of Mediterranean coral populations.
Axis-Normalized Ray-Box Intersection
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Friederichs, Fabian; Benthin, Carsten; Grogorick, Steve; Eisemann, Elmar; Magnor, Marcus; Eisemann, Martin; Bousseau, Adrien; Day, Angela
Ray-axis aligned bounding box intersection tests play a crucial role in the runtime performance of many rendering applications, driven not by complexity but mainly by the volume of tests required. While existing solutions were believed to be pretty much optimal in terms of runtime on current hardware, our paper introduces a new intersection test requiring fewer arithmetic operations compared to all previous methods. By transforming the ray we eliminate the need for one third of the traditional bounding-slab tests and achieve a speed enhancement of approximately 13.8% or 10.9%, depending on the compiler.We present detailed runtime analyses in various scenarios.
BlendSim: Simulation on Parametric Blendshapes using Spacetime Projective Dynamics
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Wu, Yuhan; Umetani, Nobuyuki; Bousseau, Adrien; Day, Angela
We propose BlendSim, a novel framework for editable simulation using spacetime optimization on the lightweight animation representation. Traditional spacetime control methods suffer from a high computational complexity, which limits their use in interactive animation. The proposed approach effectively reduces the dimensionality of the problem by representing the motion trajectories of each vertex using continuous parametric Bézier splines with variable keyframe times. Because this mesh animation representation is continuous and fully differentiable, it can be optimized such that it follows the laws of physics under various constraints. The proposed method also integrates constraints, such as collisions and cyclic motion, making it suitable for real-world applications where seamless looping and physical interactions are required. Leveraging projective dynamics, we further enhance the computational efficiency by decoupling the optimization into local parallelizable and global quadratic steps, enabling a fast and stable simulation. In addition, BlendSim is compatible with modern animation workflows and file formats, such as the glTF, making it practical way for authoring and transferring mesh animation.
Bracket Diffusion: HDR Image Generation by Consistent LDR Denoising
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Bemana, Mojtaba; Leimkühler, Thomas; Myszkowski, Karol; Seidel, Hans-Peter; Ritschel, Tobias; Bousseau, Adrien; Day, Angela
We demonstrate generating HDR images using the concerted action of multiple black-box, pre-trained LDR image diffusion models. Common diffusion models are not HDR as, first, there is no sufficiently large HDR image dataset available to re-train them, and, second, even if it was, re-training such models is impossible for most compute budgets. Instead, we seek inspiration from the HDR image capture literature that traditionally fuses sets of LDR images, called ''exposure brackets'', to produce a single HDR image. We operate multiple denoising processes to generate multiple LDR brackets that together form a valid HDR result. To this end, we introduce a brackets consistency term into the diffusion process to couple the brackets such that they agree across the exposure range they share. We demonstrate HDR versions of state-of-the-art unconditional and conditional as well as restoration-type (LDR2HDR) generative modeling.
Cage-based Deformation of Field Functions
(The Eurographics Association, 2025) Grenier, Charline; Trancho, Kevin; Zanni, Cédric; Barthe, Loïc; Günther, Tobias; Montazeri, Zahra
Implicit geometry is a popular representation for shape modelling. It provides several interesting properties, such as infinite resolution, continuity and smooth blending. However, implicit surfaces are difficult to deform as deformations need to be invertible. They are in general restricted to linear representations or more advanced translation-based deformations. We propose a method that adapts cage-based deformation to implicit surfaces while handling self-intersections in the deformed space.
Cardioid Caustics Generation with Conditional Diffusion Models
(The Eurographics Association, 2025) Uss, Wojciech; Kaliński, Wojciech; Kuznetsov, Alexandr; Anand, Harish; Kim, Sungye; Ceylan, Duygu; Li, Tzu-Mao
Despite the latest advances in generative neural techniques for producing photorealistic images, they lack generation of multi-bounce, high-frequency lighting effect like caustics. In this work, we tackle the problem of generating cardioid-shaped reflective caustics using diffusion-based generative models. We approach this problem as conditional image generation using a diffusion-based model conditioned with multiple images of geometric, material and illumination information as well as light property. We introduce a framework to fine-tune a pre-trained diffusion model and present results with visually plausible caustics.

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